convolutional lstm
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Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 114
Ramy Monir ◽  
Daniel Kostrzewa ◽  
Dariusz Mrozek

Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection.

Suting Chen ◽  
Xin Xu ◽  
Yanyan Zhang ◽  
Dongwei Shao ◽  
Song Zhang ◽  

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 400
Ghazal Farhani ◽  
Yue Zhou ◽  
Patrick Danielson ◽  
Ana Luisa Trejos

Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users’ postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users’ postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users’ postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users’ future postures based on their previous motions, the model can forecast a user’s motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed.

2022 ◽  
Vol 162 ◽  
pp. 107996
Junchuan Shi ◽  
Dikang Peng ◽  
Zhongxiao Peng ◽  
Ziyang Zhang ◽  
Kai Goebel ◽  

Karisma Trinanda Putra ◽  
Prayitno ◽  
Eko Fajar Cahyadi ◽  
Ardia Suttyawati Mamonto ◽  
Sunneng Sandino Berutu ◽  

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